IDEAS home Printed from https://ideas.repec.org/a/spr/endesu/v27y2025i3d10.1007_s10668-023-04171-3.html
   My bibliography  Save this article

Aggregated peak-load analysis and demand response potential of a residential building in Osaka, Japan

Author

Listed:
  • Nishat Tasnim Toosty

    (Kyushu University
    University of Dhaka)

  • Shota Shimoda

    (Kyushu University)

  • Aya Hagishima

    (Kyushu University
    Kyushu University)

Abstract

Global climate change has expedited the growth of renewable generation worldwide, particularly in Japan, which aims to attain carbon neutrality by 2050. Rapid increases in rooftop photovoltaic power and grid constraints in Japan have highlighted the need for efficient demand-management strategies for residential sectors. Under this background, identification of flexible consumers can be decisive for planning demand response (DR) programmes. This study analysed longitudinal data on electricity demand from 2013 to 2014 in a residential building of Osaka Prefecture, Japan. Score analyses quantified individual DR potentials of air-conditioning (AC) loads, a promising DR resource for domestic consumers. Next, the k-medoids clustering approach classified target households and quantile regression models identified household clusters with a high DR potential. Behavioural traits such as the frequency, peak-hour propensity, and grid adversity of AC use depicted the higher AC consumption, larger dwelling size, and daytime occupancy as influential characteristics to increase the potency of DR programmes. Thus, scrutinising the consumers’ attributes and AC-usage patterns, this study revealed valuable implications for ramping up DR schemes and contributed to a sustainable energy future.

Suggested Citation

  • Nishat Tasnim Toosty & Shota Shimoda & Aya Hagishima, 2025. "Aggregated peak-load analysis and demand response potential of a residential building in Osaka, Japan," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 27(3), pages 6881-6898, March.
    • Handle: RePEc:spr:endesu:v:27:y:2025:i:3:d:10.1007_s10668-023-04171-3
    DOI: 10.1007/s10668-023-04171-3
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10668-023-04171-3
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10668-023-04171-3?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Afzalan, Milad & Jazizadeh, Farrokh, 2019. "Residential loads flexibility potential for demand response using energy consumption patterns and user segments," Applied Energy, Elsevier, vol. 254(C).
    2. Nikolaos Iliopoulos & Motoharu Onuki & Miguel Esteban, 2021. "Shedding Light on the Factors That Influence Residential Demand Response in Japan," Energies, MDPI, vol. 14(10), pages 1-23, May.
    3. Klein, Konstantin & Langner, Robert & Kalz, Doreen & Herkel, Sebastian & Henning, Hans-Martin, 2016. "Grid support coefficients for electricity-based heating and cooling and field data analysis of present-day installations in Germany," Applied Energy, Elsevier, vol. 162(C), pages 853-867.
    4. Haider, Haider Tarish & See, Ong Hang & Elmenreich, Wilfried, 2016. "A review of residential demand response of smart grid," Renewable and Sustainable Energy Reviews, Elsevier, vol. 59(C), pages 166-178.
    5. Jesse Kaczmarski & Benjamin Jones & Janie Chermak, 2022. "Determinants of Demand Response Program Participation: Contingent Valuation Evidence from a Smart Thermostat Program," Energies, MDPI, vol. 15(2), pages 1-18, January.
    6. Qi, Ning & Cheng, Lin & Xu, Helin & Wu, Kuihua & Li, XuLiang & Wang, Yanshuo & Liu, Rui, 2020. "Smart meter data-driven evaluation of operational demand response potential of residential air conditioning loads," Applied Energy, Elsevier, vol. 279(C).
    7. Yamaguchi, Yohei & Chen, Chien-fei & Shimoda, Yoshiyuki & Yagita, Yoshie & Iwafune, Yumiko & Ishii, Hideo & Hayashi, Yasuhiro, 2020. "An integrated approach of estimating demand response flexibility of domestic laundry appliances based on household heterogeneity and activities," Energy Policy, Elsevier, vol. 142(C).
    8. Dyson, Mark E.H. & Borgeson, Samuel D. & Tabone, Michaelangelo D. & Callaway, Duncan S., 2014. "Using smart meter data to estimate demand response potential, with application to solar energy integration," Energy Policy, Elsevier, vol. 73(C), pages 607-619.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Song, Zhaofang & Shi, Jing & Li, Shujian & Chen, Zexu & Jiao, Fengshun & Yang, Wangwang & Zhang, Zitong, 2022. "Data-driven and physical model-based evaluation method for the achievable demand response potential of residential consumers' air conditioning loads," Applied Energy, Elsevier, vol. 307(C).
    2. Qi Huang & Aihua Jiang & Yu Zeng & Jianan Xu, 2022. "Community Flexible Load Dispatching Model Based on Herd Mentality," Energies, MDPI, vol. 15(13), pages 1-18, June.
    3. Aliakbari Sani, Sajad & Bahn, Olivier & Delage, Erick, 2022. "Affine decision rule approximation to address demand response uncertainty in smart Grids’ capacity planning," European Journal of Operational Research, Elsevier, vol. 303(1), pages 438-455.
    4. Ioanna-M. Chatzigeorgiou & Christos Diou & Kyriakos C. Chatzidimitriou & Georgios T. Andreou, 2021. "Demand Response Alert Service Based on Appliance Modeling," Energies, MDPI, vol. 14(10), pages 1-15, May.
    5. Davarzani, Sima & Pisica, Ioana & Taylor, Gareth A. & Munisami, Kevin J., 2021. "Residential Demand Response Strategies and Applications in Active Distribution Network Management," Renewable and Sustainable Energy Reviews, Elsevier, vol. 138(C).
    6. Francesco Mancini & Jacopo Cimaglia & Gianluigi Lo Basso & Sabrina Romano, 2021. "Implementation and Simulation of Real Load Shifting Scenarios Based on a Flexibility Price Market Strategy—The Italian Residential Sector as a Case Study," Energies, MDPI, vol. 14(11), pages 1-21, May.
    7. Luo, Xi & Liu, Yanfeng & Feng, Pingan & Gao, Yuan & Guo, Zhenxiang, 2021. "Optimization of a solar-based integrated energy system considering interaction between generation, network, and demand side," Applied Energy, Elsevier, vol. 294(C).
    8. Song, Yuguang & Chen, Fangjian & Xia, Mingchao & Chen, Qifang, 2022. "The interactive dispatch strategy for thermostatically controlled loads based on the source–load collaborative evolution," Applied Energy, Elsevier, vol. 309(C).
    9. Skoczkowski, Tadeusz & Bielecki, Sławomir & Wołowicz, Marcin & Sobczak, Lidia & Węglarz, Arkadiusz & Gilewski, Paweł, 2024. "Participation in demand side response. Are individual energy users interested in this?," Renewable Energy, Elsevier, vol. 232(C).
    10. Kanakadhurga, Dharmaraj & Prabaharan, Natarajan, 2022. "Peer-to-Peer trading with Demand Response using proposed smart bidding strategy," Applied Energy, Elsevier, vol. 327(C).
    11. Silva, Hendrigo Batista da & Santiago, Leonardo P., 2018. "On the trade-off between real-time pricing and the social acceptability costs of demand response," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1513-1521.
    12. Jordehi, A. Rezaee, 2019. "Optimisation of demand response in electric power systems, a review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 103(C), pages 308-319.
    13. Iolanda Saviuc & Herbert Peremans & Steven Van Passel & Kevin Milis, 2019. "Economic Performance of Using Batteries in European Residential Microgrids under the Net-Metering Scheme," Energies, MDPI, vol. 12(1), pages 1-28, January.
    14. Hu, Maomao & Xiao, Fu & Wang, Lingshi, 2017. "Investigation of demand response potentials of residential air conditioners in smart grids using grey-box room thermal model," Applied Energy, Elsevier, vol. 207(C), pages 324-335.
    15. Vallianos, Charalampos & Candanedo, José & Athienitis, Andreas, 2023. "Application of a large smart thermostat dataset for model calibration and Model Predictive Control implementation in the residential sector," Energy, Elsevier, vol. 278(PA).
    16. Fredrik Skaug Fadnes & Reyhaneh Banihabib & Mohsen Assadi, 2023. "Using Artificial Neural Networks to Gather Intelligence on a Fully Operational Heat Pump System in an Existing Building Cluster," Energies, MDPI, vol. 16(9), pages 1-33, May.
    17. Meyabadi, A. Fattahi & Deihimi, M.H., 2017. "A review of demand-side management: Reconsidering theoretical framework," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 367-379.
    18. Capper, Timothy & Kuriakose, Jaise & Sharmina, Maria, 2024. "Facilitating domestic demand response in Britain’s electricity system," Utilities Policy, Elsevier, vol. 89(C).
    19. Fernando Venâncio Mucomole & Carlos Augusto Santos Silva & Lourenço Lázaro Magaia, 2025. "Parametric Forecast of Solar Energy over Time by Applying Machine Learning Techniques: Systematic Review," Energies, MDPI, vol. 18(6), pages 1-51, March.
    20. So-Yeon Park & Ju-Hee Kim & Jungkwan Seo & Seung-Hoon Yoo, 2022. "Evaluating the Economic Benefits of Tightening Regulations on the Use of Toluene, a Hazardous Chemical, in South Korea," Sustainability, MDPI, vol. 14(11), pages 1-15, May.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:endesu:v:27:y:2025:i:3:d:10.1007_s10668-023-04171-3. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.